Research Article
Improving Anomaly Detection Accuracy Using Fuzzy Cuckoo-Inspired Clustering and Optimization Techniques
Issue:
Volume 11, Issue 2, December 2026
Pages:
63-75
Received:
16 February 2026
Accepted:
1 June 2026
Published:
22 July 2026
Abstract: Anomaly detection is a critical task for identifying unusual patterns that may indicate security breaches, fraudulent transactions, or system failures in various application domains. Conventional anomaly detection techniques often experience high false positive rates and limited adaptability when handling complex, uncertain, or evolving datasets. To overcome these limitations, this study proposes a novel Fuzzy Cuckoo-Based Clustering Technique (F-CBCT) that integrates fuzzy logic with cuckoo search-based clustering and optimization. The proposed framework employs a decision tree classifier enhanced with fuzzy membership functions, enabling effective management of uncertainty during classification. Model parameters are optimized using a hybrid strategy based on Mean Square Error (MSE) and the Silhouette Index, improving clustering quality and classification accuracy. Experimental evaluations conducted on benchmark datasets demonstrate the effectiveness of the proposed approach, achieving a 96.86% detection rate, 97.77% accuracy, a 1.297% false positive rate, and an F-measure of 98.30%. Comparative analysis with existing state-of-the-art anomaly detection methods confirms that F-CBCT consistently outperforms conventional approaches in terms of detection capability, robustness, and reliability. The proposed technique effectively reduces false alarms while maintaining high detection performance, making it a promising solution for real-world anomaly detection applications across diverse and dynamic environments.
Abstract: Anomaly detection is a critical task for identifying unusual patterns that may indicate security breaches, fraudulent transactions, or system failures in various application domains. Conventional anomaly detection techniques often experience high false positive rates and limited adaptability when handling complex, uncertain, or evolving datasets. T...
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Research Article
Investigation on Machine Learning Models for Predicting Diabetes Risk in Indian Populations
Gajendra Singh*
Issue:
Volume 11, Issue 2, December 2026
Pages:
76-84
Received:
8 July 2026
Accepted:
6 August 2026
Published:
2 September 2026
Abstract: Background: Diabetes mellitus is a major public health concern in India, with increasing prevalence driven by demographic, metabolic, behavioral, and lifestyle-related factors. Early identification of individuals at high risk of diabetes can support timely prevention and improve health outcomes. Machine learning (ML) approaches offer opportunities to identify complex and nonlinear relationships among multiple risk factors and may complement conventional statistical approaches. Objective: This study aimed to investigate and compare the performance of different ML models for predicting diabetes risk, with particular emphasis on identifying the most influential predictors and assessing the potential applicability of ML-based approaches for early risk stratification in Indian populations. Methods: A cross-sectional analytical approach was used to evaluate demographic, physiological, and lifestyle-related variables associated with diabetes risk. The study considered variables including age, body mass index (BMI), blood glucose, blood pressure, insulin, family history of diabetes, and physical activity. Data preprocessing included missing-value management, outlier identification, categorical encoding, and feature standardization. The dataset was divided into training (70%) and testing (30%) subsets using stratified sampling. Seven supervised ML algorithms—Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network—were compared. Model performance was assessed using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Pearson correlation, one-way ANOVA, and multivariate logistic regression were additionally used to examine associations between predictors and diabetes risk. Results: XGBoost demonstrated the strongest overall predictive performance, achieving an accuracy of 89.2%, precision of 0.88, recall of 0.87, F1-score of 0.87, and AUC of 0.93. Random Forest and the neural network also demonstrated strong performance, with accuracy of 87.6% and 88.5% and AUCs of 0.91 and 0.92, respectively. Glucose level was the most influential predictor, followed by BMI and age. Statistical analyses further supported the importance of these metabolic and demographic factors in diabetes risk prediction. Conclusion: Ensemble and advanced ML approaches, particularly XGBoost and Random Forest, demonstrated promising performance for diabetes risk prediction. Integrating ML with statistical analysis and interpretable AI approaches may strengthen early risk identification and support evidence-based diabetes prevention and personalized healthcare strategies in Indian populations. Further validation using larger, diverse, and multi center datasets is required before clinical implementation.
Abstract: Background: Diabetes mellitus is a major public health concern in India, with increasing prevalence driven by demographic, metabolic, behavioral, and lifestyle-related factors. Early identification of individuals at high risk of diabetes can support timely prevention and improve health outcomes. Machine learning (ML) approaches offer opportunities ...
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